CN105424147A - Granary weight detection method and device based on stack height and the bottom pressure relation - Google Patents

Abstract

The present invention relates to a granary weight detection method and device based on stack height and the bottom pressure relation, belonging to the technical field of grain weight detection. Through arranging two groups of pressure sensors at a granary bottom, a granary weight detection model W (shown in the specification) is established, the output value of each sensor is detector, and the detection of granary weight detection is realized according to the established detection model. The detection method provided by the invention has the advantages of high detection precision, low sensor performance requirement, high adaptability and robustness, convenient remote online granary number detection and granary state monitoring, the need of common use granary grain storage number remote online detection can be satisfied, the detection method is adapted to the gain storage weight detection of a variety of structural types of granaries and has a large application value, and a novel technical mean is provided for protecting national grain quantity security.

Description

Granary weight detection method and device based on relation between grain pile height and bottom pressure

Technical Field

The invention relates to a granary weight detection method and device based on the relation between the grain pile height and the bottom pressure, and belongs to the technical field of grain weight detection.

Background

The grain safety includes quantity safety and quality safety. The online grain quantity detection technology and the system research application are important guarantee technologies for national grain quantity safety, and the development of the research and application on the aspect of national grain safety has important significance and can generate huge social and economic benefits. Because of the important position of grains in national safety, the on-line detection of the quantity of grain piles is required to be accurate, rapid and reliable. Meanwhile, because the quantity of the grains is huge and the price is low, the cost of the grain pile quantity on-line detection equipment is low, and the detection equipment is simple and convenient. Therefore, the high precision of detection and the low cost of the detection system are key problems which need to be solved in the development of the online detection method for the number of the granaries.

The patent application with the application number of 201410101693.5 provides a granary grain storage quantity detection method based on a structure self-adaptive detection model, the detection method is characterized in that two circles of pressure sensors are arranged on the bottom surface of a granary, the output values of the sensors are detected, the granary weight estimation is calculated according to the established detection model, and the established detection model isThe detection model is obtained by estimating the side pressure and the bottom pressure as polynomials with respect to the average output values of the outer ring pressure sensor and the inner ring pressure sensor, respectively.

Disclosure of Invention

The invention aims to provide a granary weight detection method and device based on the relationship between the height of a grain pile and the pressure of the bottom surface, and belongs to a new reserve detection idea.

The invention provides a granary weight detection method based on the relationship between the height of a grain pile and the pressure of the bottom surface, which comprises the following steps:

1) arranging two groups of pressure sensors on the bottom surface of the granary, wherein one group of pressure sensors are inner ring sensors, the other group of pressure sensors are outer ring sensors, the outer ring sensors are arranged close to the side wall at intervals, and the inner ring sensors are arranged at a set distance from the side wall at intervals;

2) establishing a granary weight detection model according to the arrangement mode of the sensors in the step 1):

$\hat{W}=A_B\{\underset{m=0}{\overset{N_B}{\Σ}}{b}_B\left(m\right){\stackrel{\‾}{Q}}_B{\left(s_{Inner}\right)}^m+K_c^{2}ln\[1-K_p{\stackrel{\‾}{Q}}_{BF}\left(s\right)\]\underset{n=0}{\overset{N_F}{\Σ}}{b}_F\left(n\right){\stackrel{\‾}{Q}}_B{\left(s_{Outer}\right)}^n\}$

wherein A is_BIs the area of the bottom surface of the grain pile,C_Bthe circumference of the bottom surface is the length,is the average value of the output of the inner ring sensor,is the average value of the output of the outer ring sensor, b_B(m) and b_F(n) are each independentlyAndestimate coefficients of the term, m 0_B，n＝0,...,N_F，N_BAnd N_FAre respectively asAndthe order of the polynomial to be estimated is, ${\stackrel{\‾}{Q}}_{BF}\left(s\right)=\[{\stackrel{\‾}{Q}}_B\left(s_{Inner}\right)+{\stackrel{\‾}{Q}}_B\left(s_{Outer}\right)\]/2;$

3) detecting the output value of each sensor in the step 1), and calculating the estimated value of the weight of the detected granary according to the detection model in the step 2)

The calibration of each parameter in the granary weight detection model in the step 2) is as follows:

A. arranging pressure sensors in more than 6 granaries according to the mode of the step 1), feeding grains to full granaries, collecting the output value of the pressure sensor of each granary after the output value of the pressure sensor is stable, and forming a sample setWherein i is a sample point number, i is 1,2,3, M is the number of samples;respectively for ith sample pointAnda value; w_iIs the actual grain feed weight at sample point i,the corresponding granary area;

B. dividing the sample set S into three parts, optimizing and performing multiple regression on the sample set S_M、Andterm maximum order selection sample S_OAnd a test specimen S_T；

C. Given a K_PUsing an optimized and multivariate regression sample set S_MDetermining the regression parameter b by a multiple regression method_B(m) and b_F(n)；

D. According to the optimization and multiple regression sample set S_MOptimizing the parameter K using the following optimization model_P，

$Min\underset{i\∈S_M}{\Σ}{(1-\frac{{\hat{W}}_i}{W_i})}^2$

Constraint conditions are as follows: k_P＞0

$1-K_p{\stackrel{\‾}{Q}}_{BF}\left(s\right)>0;$

E. Calculating a sample set S according to a percentage error model_OAnd S_MPrediction error E (N)_B,N_F)

$E(N_B,N_F)=\underset{i\∈S_o\∪S_M}{\Σ}\frac{\left|W_i-{\hat{W}}_i\right|}{W_i}$

Setting N_BSelection Range [1, MaxN_B],N_FSelection Range [1, MaxN_F]If, if

$E(N_B^{*},N_F^{*})=\underset{1\≤N_F\≤{\mathrm{MaxN}}_F}{\underset{1\≤N_B\≤{\mathrm{MaxN}}_B}{\min }}E(N_B,N_F)$

ThenI.e. of the detection modelAndthe best maximum order sought by the term.

MaxN in said step E_BAnd MaxN_FHas a value of 4 to 10.

The detection model is obtained on the basis of a granary weight theoretical detection model, and the granary weight theoretical detection model is as follows:

$\hat{W}=A_B\{{\stackrel{\‾}{Q}}_B\left(s\right)-\frac{K_c^2}{K}\ln \[1-K_{\mathrm{\∞}}{\stackrel{\‾}{Q}}_B\left(s\right)\]{\stackrel{\‾}{Q}}_F\left(s\right)\}$

wherein,for the estimation of the weight of the grain bulk,A_Bis the area of the bottom of the grain heap C_BThe circumference of the bottom surface is the length, ${\stackrel{\‾}{Q}}_B\left(s\right)=\frac{1}{n_B}\underset{i=0}{\overset{n_B}{\Σ}}{Q}_B\left(s_i\right),{\stackrel{\‾}{Q}}_F\left(s\right)=\frac{1}{n_F}\underset{j=0}{\overset{n_F}{\Σ}}{Q}_F\left(s_j\right),$ Q_B(s)、Q_F(s) are respectively the pressure of the point s in the bottom surface and the side surface of the grain pile,is a grain pileThe bottom pressure saturation value is higher than a certain height.

The distance D between the outer ring sensor and the side wall is larger than 0 and smaller than 1 meter, and the distance D between the inner ring sensor and the side wall is larger than 2 meters.

The invention also provides a granary weight detection device based on the relation between the grain bulk height and the bottom pressure, the detection device comprises a detection unit and pressure sensors connected with the detection unit and arranged on the bottom surface of the granary, the pressure sensors are arranged in two groups, one group is an inner ring sensor, the other group is an outer ring sensor, the outer ring sensor is arranged close to the side wall of the granary at intervals, the inner ring sensor is arranged at a set distance from the side wall of the granary at intervals, one or more modules are executed in the detection unit, and the one or more modules are used for executing the following steps:

1) establishing a granary weight detection model:

$\hat{W}=A_B\{\underset{m=0}{\overset{N_B}{\Σ}}{b}_B\left(m\right){\stackrel{\‾}{Q}}_B{\left(s_{Inner}\right)}^m+K_c^{2}ln\[1-K_p{\stackrel{\‾}{Q}}_{BF}\left(s\right)\]\underset{n=0}{\overset{N_F}{\Σ}}{b}_F\left(n\right){\stackrel{\‾}{Q}}_B{\left(s_{Outer}\right)}^n\}$

wherein A is_BIs the area of the bottom surface of the grain pile,C_Bthe circumference of the bottom surface is the length,is the average value of the output of the inner ring sensor,is the average value of the output of the outer ring sensor, b_B(m) and b_F(n) are each independentlyAndestimate coefficients of the term, m 0_B，n＝0,...,N_F，N_BAnd N_FAre respectively asAndthe order of the polynomial to be estimated is, ${\stackrel{\‾}{Q}}_{BF}\left(s\right)=\[{\stackrel{\‾}{Q}}_B\left(s_{Inner}\right)+{\stackrel{\‾}{Q}}_B\left(s_{Outer}\right)\]/2;$

2) detecting the output value of each sensor, and calculating the estimated value of the weight of the detected granary according to the established granary weight detection model

The calibration of each parameter in the granary weight detection model is as follows:

A. arranging pressure sensors in more than 6 granaries according to the mode of claim 6, feeding grains to full granaries, collecting the output value of the pressure sensor of each granary after the output value of the pressure sensor is stable, and forming a sample setWherein i is a sample point number, i is 1,2,3, M is the number of samples;respectively for ith sample pointAnda value; w_iIs the actual grain feed weight at sample point i,the corresponding granary area;

B. dividing the sample set S into three parts, optimizing and performing multiple regression on the sample set S_M、Andterm maximum order selection sample S_OAnd a test specimen S_T；

C. Given a K_PUsing an optimized and multivariate regression sample set S_MDetermining the regression parameter b by a multiple regression method_B(m) and b_F(n)；

D. According to the optimization and multiple regression sample set S_MOptimizing the parameter K using the following optimization model_P，

$Min\underset{i\∈S_M}{\Σ}{(1-\frac{{\hat{W}}_i}{W_i})}^2$

Constraint conditions are as follows: k_P＞0

$1-K_p{\stackrel{\‾}{Q}}_{BF}\left(s\right)>0;$

E. Calculating a sample set S according to a percentage error model_OAnd S_MPrediction error E (N)_B,N_F ⁾

$E(N_B,N_F)=\underset{i\∈S_o\∪S_M}{\Σ}\frac{\left|W_i-{\hat{W}}_i\right|}{W_i}$

Setting N_BSelection Range [1, MaxN_B],N_FSelection Range [1, MaxN_F]If, if

$E(N_B^{*},N_F^{*})=\underset{1\≤N_F\≤{\mathrm{MaxN}}_F}{\underset{1\≤N_B\≤{\mathrm{MaxN}}_B}{\min }}E(N_B,N_F)$

ThenI.e. of the detection modelAndthe best maximum order sought by the term.

MaxN in said step E_BAnd MaxN_FHas a value of 4 to 10.

The detection model is obtained on the basis of a granary weight theoretical detection model, and the granary weight theoretical detection model is as follows:

$\hat{W}=A_B\{{\stackrel{\‾}{Q}}_B\left(s\right)-\frac{K_c^2}{K}\ln \[1-K_{\mathrm{\∞}}{\stackrel{\‾}{Q}}_B\left(s\right)\]{\stackrel{\‾}{Q}}_F\left(s\right)\}$

wherein,for the estimation of the weight of the grain bulk,A_Bis the area of the bottom of the grain heap C_BThe circumference of the bottom surface is the length, ${\stackrel{\‾}{Q}}_B\left(s\right)=\frac{1}{n_B}\underset{i=0}{\overset{n_B}{\Σ}}{Q}_B\left(s_i\right),{\stackrel{\‾}{Q}}_F\left(s\right)=\frac{1}{n_F}\underset{j=0}{\overset{n_F}{\Σ}}{Q}_F\left(s_j\right),$ Q_B(s)、Q_F(s) are respectively the pressure of the point s in the bottom surface and the side surface of the grain pile,for the pressure saturation of the bottom surface when the grain pile is far above a certain heightAnd a value.

The distance D between the outer ring sensor and the side wall is larger than 0 and smaller than 1 meter, and the distance D between the inner ring sensor and the side wall is larger than 2 meters.

The granary weight detection model has the beneficial effects that the granary weight detection model is established by arranging two groups of pressure sensors on the bottom surface of the granary $\hat{W}=A_B\{\underset{m=0}{\overset{N_B}{\Σ}}{b}_B\left(m\right){\stackrel{\‾}{Q}}_B{\left(s_{Inner}\right)}^m+K_c^{2}ln\[1-K_p{\stackrel{\‾}{Q}}_{BF}\left(s\right)\]\underset{n=0}{\overset{N_F}{\Σ}}{b}_F\left(n\right){\stackrel{\‾}{Q}}_B{\left(s_{Outer}\right)}^n\},$ And detecting the output value of each sensor, and realizing the detection of the weight of the granary according to the established detection model. The detection method provided by the invention has the characteristics of high detection precision, low requirement on the performance of the sensor, strong adaptability and robustness, convenience for remote online detection of the number of the granaries and monitoring of the state of the granaries and the like, can meet the requirement of remote online detection of the number of the stored grains of the granaries which are usually used, is suitable for detecting the number of the stored grains of various granaries, has huge application value, and provides a new technical means for guaranteeing the safety of the number of the grains in China.

Drawings

FIG. 1 is a schematic diagram of a horizontal warehouse floor pressure sensor arrangement model;

FIG. 2 is a schematic diagram of a model of the arrangement of the pressure sensors on the bottom surface of the silo;

FIG. 3 is a schematic diagram showing the error of the weight prediction of the modeled sample in test example 2 of the present invention;

FIG. 4 is a graph showing the prediction error of all sample weights in test example 2 of the present invention;

fig. 5 is a flow chart of the implementation of the granary weight detecting method of the present invention.

Detailed Description

The following further describes embodiments of the present invention with reference to the drawings.

Embodiment of granary weight detection method based on relation between grain pile height and bottom pressure

The granary weight detection method based on the relation between the height of the grain pile and the pressure of the bottom surface calculates the weight of the granary according to the established detection model by establishing the corresponding granary weight detection model, and specifically introduces the theoretical premise obtained by the model, the corresponding granary sensor arrangement, the model derivation and the parameter calibration in sequence.

1. Granary weight theoretical detection model

The commonly used grain silos are of the type of horizontal silo, squat silo, silo and the like, after grains are put into the silo, the top of a grain pile is required to be flattened, the shape of the grain pile of the horizontal silo is approximately a cube with different sizes, and the shape of the grain pile of the squat silo and the silo is approximately a cylinder with different sizes. The stress analysis of the grain stack can be used to obtain that the weight of the grain stack in the granary and the pressure distribution of the granary have the following relationship.

$\hat{W}=A_B\[{\stackrel{\‾}{Q}}_B\left(s\right)+\frac{C_B}{A_B}{\mathrm{Hf}}_F{\stackrel{\‾}{Q}}_F\left(s\right)\]---\left(1\right)$

Wherein,for grain bulk weight estimation, A_BIs the area of the bottom of the grain heap C_BThe bottom circumference, H the grain bulk height, f_FThe average friction coefficient between the side surface of the grain pile and the side surface of the granary; Q_B(s)、Q_F(s) are respectively the pressure at the midpoint s of the bottom surface and the side surface of the grain pile.

According to the Janssen model, the approximate relationship between the pressure at the bottom of the granary and the height of the grain pile can be deduced as shown in the following formula.

${\stackrel{\‾}{Q}}_B\left(s\right)={{\stackrel{\‾}{Q}}_B}_{\mathrm{\∞}}\left(s\right)\[\begin{array}{cc}1-\exp & (-H/\mathrm{\λ})\end{array}\]---\left(2\right)$

Wherein,the characteristic height of the grain bulk of the granary is shown, and K is a pressure steering coefficient;the bottom pressure saturation value when the grain pile is far higher than the characteristic height. Can be derived from the formula (2)

$H=-\frac{K_c}{f_{F}K}ln\[1-K_{\mathrm{\∞}}{\stackrel{\‾}{Q}}_B\left(s\right)\]---\left(3\right)$

Wherein, $K_{\mathrm{\∞}}=\frac{1}{{\stackrel{\‾}{Q}}_{B\mathrm{\∞}}\left(s\right)};K_c=\frac{C_B}{A_B}.$

when formula (3) is substituted for formula (1), there are

$\hat{W}=A_B\{{\stackrel{\‾}{Q}}_B\left(s\right)-\frac{K_c^2}{K}\ln \[1-K_{\mathrm{\∞}}{\stackrel{\‾}{Q}}_B\left(s\right)\]{\stackrel{\‾}{Q}}_F\left(s\right)\}---\left(4\right)$

Wherein,for the estimation of the weight of the grain bulk,A_Bis the area of the bottom of the grain heap C_BThe circumference of the bottom surface is the length, ${\stackrel{\‾}{Q}}_B\left(s\right)=\frac{1}{n_B}\underset{i=0}{\overset{n_B}{\Σ}}{Q}_B\left(s_i\right),{\stackrel{\‾}{Q}}_F\left(s\right)=\frac{1}{n_F}\underset{j=0}{\overset{n_F}{\Σ}}{Q}_F\left(s_j\right),$ Q_B(s)、Q_F(s) are respectively the pressure of the point s in the bottom surface and the side surface of the grain pile,the bottom pressure saturation value when the grain pile is far higher than a certain height.

2. Granary pressure sensor arrangement

For the flat house in common useThe granary comprises a granary and a silo, wherein pressure sensors are arranged on the bottom surface of the granary according to an outer ring and an inner ring, as shown in figures 1 and 2, the circles are the arrangement positions of the pressure sensors, the distances between the outer ring pressure sensors and a side wall are D, and the distances between the inner ring pressure sensors and the side wall are D. Obviously, when d is equal to 0, the bottom pressure at the outer ring is also the pressure at the bottom of the side surface. And can therefore be described by the output value of the outer ring pressure sensorSize, described by inner ring pressure sensor output valueSize.

Practical experiments show that when the distance d between the outer ring pressure sensor and the side wall is 0, the output value of the pressure sensor is describedThe accuracy of the method is improved, but the fluctuation of the output value is obviously increased, so that the accuracy of the detection model is influenced, and therefore d is more than 0 meter and less than 1 meter to ensure the accuracy of the model. The larger the distance D between the inner ring sensor and the side wall is, the output value description of the pressure sensor isThe effectiveness of (D) is improved, therefore, under the condition of conveniently loading and unloading the grains, D is properly increased, so that D can be taken>2m, generally about 3 m. In order to ensure the universality of the detection model, the distances D and D between the inner and outer ring pressure sensors of each granary and the side wall are the same, the number of the two rings of sensors is 6-10, and the distance between the sensors is not less than 1 m.

3. Granary weight detection model derivation

The granary bottom surface two-circle sensor arrangement model shown in figures 1 and 2 is adopted, and the average value of the output values of the outer-circle sensors is utilizedConstructing estimates of granary side pressureUsing mean value of output values of inner ring sensorsAnd constructing the pressure estimation of the bottom surface of the granary. For the theoretical model of granary weight detection shown in formula (4), let

$H_{\mathrm{\∞}}=ln\[1-K_{\mathrm{\∞}}{\stackrel{\‾}{Q}}_B\left(s\right)\]---\left(5\right)$

Order to ${\stackrel{\‾}{Q}}_{BF}\left(s\right)=\[{\stackrel{\‾}{Q}}_B\left(s_{Inner}\right)+{\stackrel{\‾}{Q}}_B\left(s_{Outer}\right)\]/2,$ In the structural formula (5)Is estimated as

${\widehat{\stackrel{\‾}{Q}}}_B\left(s\right)=b_{BF}{\stackrel{\‾}{Q}}_{BF}\left(s\right)---\left(6\right)$

Then H_∞Estimated as

${\hat{H}}_{\mathrm{\∞}}=ln(1-K_P{\stackrel{\‾}{Q}}_{BF}(s\left)\right)---\left(7\right)$

Wherein K_P＝K_∞b_BF. By substituting formula (7) for formula (4)

$\hat{W}=A_B\{{\stackrel{\‾}{Q}}_B\left(s\right)-\frac{K_c^2}{K}\ln \[1-K_p{\stackrel{\‾}{Q}}_{BF}\left(s\right)\]{\stackrel{\‾}{Q}}_F\left(s\right)\}---\left(8\right)$

For formula (5), use is made ofPolynomial constructionEstimated as

${\widehat{\stackrel{\‾}{Q}}}_B\left(s\right)=\underset{m=0}{\overset{N_B}{\Σ}}{b}_B\left(m\right){\stackrel{\‾}{Q}}_B{\left(s_{Inner}\right)}^m---\left(9\right)$

$\frac{1}{K}{\widehat{\stackrel{\‾}{Q}}}_F\left(s\right)=\underset{n=0}{\overset{N_F}{\Σ}}{b}_F\left(n\right){\stackrel{\‾}{Q}}_B{\left(s_{Outer}\right)}^n---\left(10\right)$

Wherein, b_B(m) and b_F(n) are each independentlyAndestimate coefficients of the term, m 0_B，n＝0,...,N_F，N_BAnd N_FAre respectively asAndestimated polynomial order. When formula (9) and formula (10) are substituted for formula (8), there are

$\hat{W}=A_B\{\underset{m=0}{\overset{N_B}{\Σ}}{b}_B\left(m\right){\stackrel{\‾}{Q}}_B{\left(s_{Inner}\right)}^m+K_c^{2}ln\[1-K_p{\stackrel{\‾}{Q}}_{BF}\left(s\right)\]\underset{n=0}{\overset{N_F}{\Σ}}{b}_F\left(n\right){\stackrel{\‾}{Q}}_B{\left(s_{Outer}\right)}^n\}---\left(11\right)$

The formula (8) is the granary weight detection model based on the relationship between the grain bulk height and the bottom pressure.

4. Calibration of parameters in detection model

For a given sensor, grain type and bin type, the established weight detection model of the grain bin needs to be calibrated, that is, each parameter in the formula (11) is solved, and the specific process is as follows:

A. arranging pressure sensors in more than 6 granaries according to the mode of the step 1), feeding grains to full granaries, collecting the output value of the pressure sensor of each granary after the output value of the pressure sensor is stable, and forming a sample setWherein i is a sample point number, i is 1,2,3, M is the number of samples;respectively for ith sample pointAnda value; w_iIs the actual grain feed weight at sample point i,the corresponding granary area;

B. dividing the sample set S into three parts, optimizing and performing multiple regression on the sample set S_M、Andterm maximum order selection sample S_OAnd a test specimen S_T；

C. Given a K_PUsing an optimized and multivariate regression sample set S_MDetermining the regression parameter b by a multiple regression method_B(m) and b_F(n)；

D. According to the optimization and multiple regression sample set S_MOptimizing the parameter K using the following optimization model_P，

$Min\underset{i\∈S_M}{\Σ}{(1-\frac{{\hat{W}}_i}{W_i})}^2$

Constraint conditions are as follows: k_P＞0

$1-K_p{\stackrel{\‾}{Q}}_{BF}\left(s\right)>0;$

E. Sample set S according to percentage error model_OAnd S_MPrediction error E (N)_B,N_F)

$E(N_B,N_F)=\underset{i\∈S_o\∪S_M}{\Σ}\frac{\left|W_i-{\hat{W}}_i\right|}{W_i}$

Setting N_BSelection Range [1, MaxN_B],N_FSelection Range [1, MaxN_F]，MaxN_BAnd MaxN_FIs 4-10, if

$E(N_B^{*},N_F^{*})=\underset{1\≤N_F\≤{\mathrm{MaxN}}_F}{\underset{1\≤N_B\≤{\mathrm{MaxN}}_B}{\min }}E(N_B,N_F)$

ThenI.e. of the detection modelAndthe best maximum order sought by the term.

Second, based on the grain pile height and the pressure relation of the bottom surface of the granary weight detection device embodiment

The detection device provided by the invention comprises a detection unit and pressure sensors which are connected with the detection unit and arranged on the bottom surface of the granary, wherein the pressure sensors are arranged in two groups, one group is an inner ring sensor, the other group is an outer ring sensor, the outer ring sensors are arranged at intervals close to the side wall of the granary, and the inner ring sensors are arranged at intervals at a set distance from the side wall of the granary, as shown in fig. 1 and 2. The detection unit may be a single chip, a DSP, a PLC, an MCU, or the like, and one or more modules may be implemented in the detection unit, where the modules may be located in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art, and the storage medium may be coupled to the detection unit so that the detection unit can read information from the storage medium, or the storage medium may be a component of the detection unit. One or more modules are configured to perform the steps of:

1) establishing a granary weight detection model:

$\hat{W}=A_B\{\underset{m=0}{\overset{N_b}{\Σ}}{b}_B\left(m\right){\stackrel{\‾}{Q}}_B{\left(s_{Inner}\right)}^m+K_c^{2}ln\[1-K_p{\stackrel{\‾}{Q}}_{BF}\left(s\right)\]\underset{n=0}{\overset{N_F}{\Σ}}{b}_F\left(n\right){\stackrel{\‾}{Q}}_B{\left(s_{Outer}\right)}^n\}$

wherein A is_BIs made of grainThe area of the bottom surface of the pile,C_Bthe circumference of the bottom surface is the length,is the average value of the output of the inner ring sensor,is the average value of the output of the outer ring sensor, b_B(m) and b_F(n) are each independentlyAndestimate coefficients of the term, m 0_B，n＝0,...,N_F，N_BAnd N_FAre respectively asAndthe order of the polynomial to be estimated is, ${\stackrel{\‾}{Q}}_{BF}\left(s\right)=\[{\stackrel{\‾}{Q}}_B\left(s_{Inner}\right)+{\stackrel{\‾}{Q}}_B\left(s_{Outer}\right)\]/2;$

2) detecting the output value of each sensor, and calculating the estimated value of the weight of the detected granary according to the established granary weight detection model

The derivation of the granary weight detection model and the calibration process of the model parameters are described in detail in the embodiment of the method, and are not described herein again.

Third, testing example and result analysis

Test example 1

The horizontal warehouse adopted by the detection example has the length of 9m, the width of 4.2m and the area of 37.8m²，C_B/A_B0.698. The granaries all belong to small-sized granaries C_B/A_BIs relatively large. According to the pressure sensor arrangement model shown in fig. 1, for a horizontal warehouse, the pressure sensors are arranged in 2 circles, 8 inner circles and 10 outer circles, and 18 pressure sensors are provided.

The experimental grain type is corn, the weight is about 160 tons, and the experiment is carried out for 4 times. Taking MaxN_B10 and MaxN_F10. Because the number of samples is too small, 1-3 experiments are used as regression samples, and experiment 4 is used as the highest order number selection sample and test sample. The model parameters established according to equation (11) are shown in tables 1 to 2, and the weight prediction results are shown in tables 3 to 6, where table 3 is the calculated result of the reserve weight of experiment 1, table 4 is the calculated result of the reserve weight of experiment 2, table 5 is the calculated result of the reserve weight of experiment 3, table 6 is the calculated result of the reserve weight of experiment 4, and the total prediction error of 4 experiments is 21.7668.

TABLE 1

TABLE 2

Taking 1-3 experiments as regression samples, taking experiment 4 as test samples, selecting samples without highest degree, establishing model parameters according to formula (11) as shown in tables 7 and 8, and weight prediction results as shown in tables 9-12, wherein table 9 is the calculation result of the reserve weight of experiment 1, table 10 is the calculation result of the reserve weight of experiment 2, table 11 is the calculation result of the reserve weight of experiment 3, table 12 is the calculation result of the reserve weight of experiment 4, the total prediction error of the experiments of 1-3 times participating in modeling is 10.715, and the total prediction error of the experiments of 4 times is 27.0091.

TABLE 7

TABLE 8

As can be seen from tables 3 and 6, the granary weight detection model provided by the invention has ideal characteristicsThe modeling accuracy and the prediction accuracy were compared with the results of the weight calculation of the test specimens shown in tables 6 and 12, and it can be seen that the weight calculation of the test specimens was performed by introducingAndterm maximum order selection sample S_OThe prediction capability of the model can be obviously improved.

Detection example 2

Taking 3 granaries of Hongze and Qinhe as an example, the grain types of the three granaries are wheat and rice, the storage weights are 2455.6 tons, 2009.98 tons and 2100 tons respectively, and 501 detection samples are obtained by detection by adopting sensors different from those in the detection example 1. 297 samples were selected as modeling samples and 197 samples were selected as multiple regression samples S_MAnd 100 are asAndterm maximum order selection sample S_OAnd the rest is used as a test sample. The model parameters according to equation (11) are shown in tables 13 to 14, and the weight prediction results are shown in fig. 3 and 4. Wherein, fig. 3 is a schematic diagram of errors of weight prediction using a modeling sample, and fig. 4 is a schematic diagram of errors of weight prediction using all samples.

Watch 13

TABLE 14

As can be seen from fig. 3 and 4, the prediction errors of all the detection points are less than 0.4%, which can meet the requirement of detecting the grain weight stored in the granary, and this also proves the effectiveness of the proposed model and modeling method of equation (11).

Specifically, the granary weight detection method and device based on the relationship between the grain bulk height and the bottom pressure provided by the invention can be implemented according to the implementation mode shown in fig. 5, and the specific steps are implemented as follows:

(1) system configuration

And selecting a specific pressure sensor, and configuring corresponding systems for data acquisition, data transmission and the like.

(2) Bottom surface pressure sensor mounting

The arrangement of the sensors of the horizontal warehouse is shown in figure 1, the arrangement of the silo is shown in figure 2, the pressure sensors on the bottom surface are arranged according to two circles of an outer circle and an inner circle, the distances between the outer circle pressure sensors and the side wall are D & gt 0 and D & lt 1 meter, and the distances between the inner circle pressure sensors and the side wall are D & gt 2 meters. The number of the two circles of sensors is 6-10, and the distance between the sensors is not less than 1 m.

(3) System calibration and model modeling

For given sensors, grain types and bin types, if the system is not calibrated, arranging pressure sensors in more than 6 bins, feeding grains to full bins, collecting the output values of the pressure sensors in the bins after the output values of the pressure sensors are stable, and forming a sample setWherein i is a sample point number, i is 1,2,3, M is the number of samples;respectively for ith sample pointAnda value; w_iIs the actual grain feed weight at sample point i,corresponding to the area of the granary. Dividing the sample set S into three parts, optimizing and performing multiple regression on the sample set S_M、Andterm maximum order selection sample S_OAnd a test specimen S_T. According to the optimization and multiple regression sample set S_MDetermining an optimization parameter K by means of an optimization algorithm_P. According to the optimization and multiple regression sample set S_MDetermining the regression parameter b in the formula (11) by using a regression method_B(m) and b_F(n) and selecting a sample set S according to the established regression model and the maximum order_OOptimizingMaximum order number N of polynomial_BAnd N_FThereby constructing a granary weight detection model shown in formula (11).

(4) And (5) detecting the weight of the real bin.

And if the system is calibrated, detecting the output of the bottom surface pressure sensor and detecting the grain storage quantity of the granary by using the model shown in the formula (11).

While the present invention has been described with reference to specific embodiments, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in the embodiments without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. The granary weight detection method based on the relationship between the grain pile height and the bottom pressure is characterized by comprising the following steps of:

1) arranging two groups of pressure sensors on the bottom surface of the granary, wherein one group of pressure sensors are inner ring sensors, the other group of pressure sensors are outer ring sensors, the outer ring sensors are arranged close to the side wall at intervals, and the inner ring sensors are arranged at a set distance from the side wall at intervals;

2) establishing a granary weight detection model according to the arrangement mode of the sensors in the step 1):

$\hat{W}=A_B\{\underset{m=0}{\overset{N_B}{\Σ}}{b}_B\left(m\right){\stackrel{\‾}{Q}}_B{\left(s_{Inner}\right)}^m+K_c^{2}ln\[1-K_p{\stackrel{\‾}{Q}}_{BF}\left(s\right)\]\underset{n=0}{\overset{N_F}{\Σ}}{b}_F\left(n\right){\stackrel{\‾}{Q}}_B{\left(s_{Outer}\right)}^n\}$

wherein A is_BIs the area of the bottom surface of the grain pile,C_Bthe circumference of the bottom surface is the length,is the average value of the output of the inner ring sensor,is the average value of the output of the outer ring sensor, b_B(m) and b_F(n) are each independentlyAndestimate coefficients of the term, m 0_B，n＝0,...,N_F，N_BAnd N_FAre respectively asAndthe order of the polynomial to be estimated is, ${\stackrel{\‾}{Q}}_{BF}\left(s\right)=\[{\stackrel{\‾}{Q}}_B\left(s_{Inner}\right)+{\stackrel{\‾}{Q}}_B\left(s_{Outer}\right)\]/2;$

3) detecting the output value of each sensor in the step 1), and calculating the estimated value of the weight of the detected granary according to the detection model in the step 2)

2. The granary weight detection method based on the relation between the height of the grain pile and the pressure on the bottom surface according to claim 1, wherein the calibration of each parameter in the granary weight detection model in the step 2) is as follows:

A. arranging pressure sensors in more than 6 granaries according to the mode of the step 1), feeding grains to full granaries, collecting the output value of the pressure sensor of each granary after the output value of the pressure sensor is stable, and forming a sample setWherein i is a sample point number, i is 1,2,3, M is the number of samples;respectively for ith sample pointAnda value; w_iIs the actual grain feed weight at sample point i,the corresponding granary area;

B. dividing the sample set S into three parts, optimizing and performing multiple regression on the sample set S_M、Andterm maximum order selection sample S_OAnd a test specimen S_T；

C. Given a K_PUsing an optimized and multivariate regression sample set S_MDetermining the regression parameter b by a multiple regression method_B(m) and b_F(n)；

D. According to the optimization and multiple regression sample set S_MOptimizing the parameter K using the following optimization model_P，

$Min\underset{i\∈S_M}{\Σ}{(1-\frac{{\hat{W}}_i}{W_i})}^2$

Constraint conditions are as follows: k_P＞0

$1-K_p{\stackrel{\‾}{Q}}_{BF}\left(s\right)>0;$

E. Calculating a sample set S according to a percentage error model_OAnd S_MPrediction error E (N)_B,N_F)

$E(N_B,N_F)=\underset{i\∈S_o\∪S_M}{\Σ}\frac{|W_i-{\hat{W}}_i|}{W_i}$

Setting N_BSelection Range [1, MaxN_B],N_FSelection Range [1, MaxN_F]If, if

$E\left(N_B^{*},N_F^{*}\right)=\underset{1\≤N_F\≤{\mathrm{MaxN}}_F}{\underset{1\≤N_B\≤{\mathrm{MaxN}}_B}{\min }}E(N_B,N_F)$

ThenI.e. of the detection modelAndthe best maximum order sought by the term.

3. The method of claim 2, wherein the MaxN step E is a step of measuring the weight of the grain bin based on the relationship between the height of the grain bulk and the pressure at the bottom of the grain bin_BAnd MaxN_FHas a value of 4 to 10.

4. The granary weight detection method based on the relation between the grain bulk height and the bottom pressure according to claim 3, wherein the detection model is obtained on the basis of a granary weight theoretical detection model, and the granary weight theoretical detection model is as follows:

$\hat{W}=A_B\{{\stackrel{\‾}{Q}}_B\left(s\right)-\frac{K_c^2}{K}\ln \[1-K_{\mathrm{\∞}}{\stackrel{\‾}{Q}}_B\left(s\right)\]{\stackrel{\‾}{Q}}_F\left(s\right)\}$

wherein,for the estimation of the weight of the grain bulk,A_Bis the area of the bottom of the grain heap C_BThe circumference of the bottom surface is the length, ${\stackrel{\‾}{Q}}_B\left(s\right)=\frac{1}{n_B}\underset{i=0}{\overset{n_B}{\Σ}}{Q}_B\left(s_i\right),{\stackrel{\‾}{Q}}_F\left(s\right)=\frac{1}{n_F}\underset{j=0}{\overset{n_F}{\Σ}}{Q}_F\left(s_j\right),$ Q_B(s)、Q_F(s) are respectively the pressure of the point s in the bottom surface and the side surface of the grain pile,the bottom pressure saturation value when the grain pile is far higher than a certain height.

5. The grain bin weight detection method based on the relation between the grain bulk height and the bottom pressure according to claim 1, wherein the distance D between the outer ring sensor and the side wall is greater than 0 and less than 1 meter, and the distance D between the inner ring sensor and the side wall is greater than 2 meters.

6. The utility model provides a granary weight detection device based on granary height and bottom surface pressure relation, its characterized in that, this detection device includes detecting element and is connected with detecting element and sets up the pressure sensor in the granary bottom surface, pressure sensor divides two sets of arrangements, and a set of inner circle sensor, a set of outer lane sensor that is, outer lane sensor are close to granary side wall interval arrangement, and inner circle sensor sets for distance and interval arrangement apart from granary side wall, the execution has one or more module in the detecting element, one or more module are used for carrying out following step:

1) establishing a granary weight detection model:

$\hat{W}=A_B\{\underset{m=0}{\overset{N_B}{\Σ}}{b}_B\left(m\right){\stackrel{\‾}{Q}}_B{\left(s_{Inner}\right)}^m+K_c^{2}ln\[1-K_p{\stackrel{\‾}{Q}}_{BF}\left(s\right)\]\underset{n=0}{\overset{N_F}{\Σ}}{b}_F\left(n\right){\stackrel{\‾}{Q}}_B{\left(s_{Outer}\right)}^n\}$

wherein A is_BIs the area of the bottom surface of the grain pile,C_Bthe circumference of the bottom surface is the length,is the average value of the output of the inner ring sensor,is the average value of the output of the outer ring sensor, b_B(m) and b_F(n) are each independentlyAndestimate coefficients of the term, m 0_B，n＝0,...,N_F，N_BAnd N_FAre respectively asAndthe order of the polynomial to be estimated is, ${\stackrel{\‾}{Q}}_{BF}\left(s\right)=\[{\stackrel{\‾}{Q}}_B\left(s_{Inner}\right)+{\stackrel{\‾}{Q}}_B\left(s_{Outer}\right)\]/2;$

2) detecting the output value of each sensor, and calculating the estimated value of the weight of the detected granary according to the established granary weight detection model

7. The granary weight detection device based on the relation between the height of the grain pile and the pressure on the bottom surface according to claim 6, wherein the calibration of each parameter in the granary weight detection model is as follows:

A. arranging pressure sensors in more than 6 granaries according to the mode of claim 6, feeding grains to full granaries, collecting the output value of the pressure sensor of each granary after the output value of the pressure sensor is stable, and forming a sample setWherein i is the same asThe point number, i ═ 1,2, 3.., M is the number of samples;respectively for ith sample pointAnda value; w_iIs the actual grain feed weight at sample point i,the corresponding granary area;

B. dividing the sample set S into three parts, optimizing and performing multiple regression on the sample set S_M、Andterm maximum order selection sample S_OAnd a test specimen S_T；

C. Given a K_PUsing an optimized and multivariate regression sample set S_MDetermining the regression parameter b by a multiple regression method_B(m) and b_F(n)；

D. According to the optimization and multiple regression sample set S_MOptimizing the parameter K using the following optimization model_P，

$Min\underset{i\∈S_M}{\Σ}{(1-\frac{{\hat{W}}_i}{W_i})}^2$

Constraint conditions are as follows: k_P＞0

$1-K_p{\stackrel{\‾}{Q}}_{BF}\left(s\right)>0;$

E. Calculating a sample set S according to a percentage error model_OAnd S_MPrediction error E (N)_B,N_F)

$E(N_B,N_F)=\underset{i\∈S_o\∪S_M}{\Σ}\frac{|W_i-{\hat{W}}_i|}{W_i}$

Setting N_BSelection Range [1, MaxN_B],N_FSelection Range [1, MaxN_F]If, if

$E\left(N_B^{*},N_F^{*}\right)=\underset{1\≤N_F\≤{\mathrm{MaxN}}_F}{\underset{1\≤N_B\≤{\mathrm{MaxN}}_B}{\min }}E(N_B,N_F)$

ThenI.e. of the detection modelAndthe best maximum order sought by the term.

8. The grain bin weight detection based on the relationship of grain bulk height to floor pressure according to claim 7Means for MaxN in said step E_BAnd MaxN_FHas a value of 4 to 10.

9. The grain bin weight detection device based on the relation between the grain bulk height and the floor pressure as claimed in claim 8, wherein the detection model is obtained on the basis of a theoretical detection model of the grain bin weight, and the theoretical detection model of the grain bin weight is as follows:

$\hat{W}=A_B\{{\stackrel{\‾}{Q}}_B\left(s\right)-\frac{K_c^2}{K}\ln \[1-K_{\mathrm{\∞}}{\stackrel{\‾}{Q}}_B\left(s\right)\]{\stackrel{\‾}{Q}}_F\left(s\right)\}$

wherein,for the estimation of the weight of the grain bulk,A_Bis the area of the bottom of the grain heap C_BThe circumference of the bottom surface is the length, ${\stackrel{\‾}{Q}}_B\left(s\right)=\frac{1}{n_B}\underset{i=0}{\overset{n_B}{\Σ}}{Q}_B\left(s_i\right),{\stackrel{\‾}{Q}}_F\left(s\right)=\frac{1}{n_F}\underset{j=0}{\overset{n_F}{\Σ}}{Q}_F\left(s_j\right),$ Q_B(s)、Q_F(s) are respectively the pressure of the point s in the bottom surface and the side surface of the grain pile,the bottom pressure saturation value when the grain pile is far higher than a certain height.

10. The grain bin weight detecting device based on the relation between the grain bulk height and the bottom pressure according to claim 1, wherein the distance D between the outer ring sensor and the side wall is greater than 0 and less than 1 meter, and the distance D between the inner ring sensor and the side wall is greater than 2 meters.